Agree or Disagree Questions: 50+ Examples for Better Surveys

Agree or disagree questions, statements respondents rate on a scale from strongly disagree to strongly agree, are among the most widely used survey formats in research. They're also one of the easiest formats to misuse: a leading or double-barrelled agreement statement can quietly manufacture the exact result the researcher was hoping to avoid. This guide covers when agree or disagree questions genuinely work, when they don't, and includes 50+ ready-to-use examples across ten categories, grounded in the same structured survey questions discipline behind any reliable research instrument.

Quick Answer

Agree or disagree questions in 20 seconds:

• What they are: Statements respondents rate on an agreement scale, typically 5-point (Strongly disagree to Strongly agree), measuring attitudes rather than facts
• Why they're popular: Fast to answer, easy to analyse, and effective at measuring attitudes and beliefs that don't reduce cleanly to a yes/no
• The risk: Leading wording, double-barrelled statements, and acquiescence bias (the tendency to agree regardless of content) can quietly distort results
• What's in this guide: 50+ examples across 10 categories, the mistakes that undermine this format specifically, and when a different format would serve you better
• The measurement note: These are Likert-type items; the deeper legality of averaging them is covered separately in ordinal scale

Introduction

"Strongly disagree to strongly agree" might be the single most reused question format in survey research, and for good reason: it's fast for respondents, clean to analyse, and genuinely effective at capturing attitudes that a yes/no question would flatten. It's also deceptively easy to write badly. A subtly leading statement, "Our support team resolves issues quickly," invites a different answer than a neutral one would, and most researchers never notice the lean because the format itself feels objective.

This guide treats the format with the respect it deserves. What agree or disagree questions actually are, why researchers reach for them so often, exactly when they work and when a different format would serve the research better, 50+ ready-to-use examples organised into ten categories, the mistakes specific to this format, better alternatives worth considering, and the best practices that keep agreement scales honest.

What Are Agree or Disagree Questions?

Agree or disagree questions present a statement and ask respondents to indicate their level of agreement, typically on a 5-point scale running from "Strongly disagree" to "Strongly agree," sometimes extended to 7 points for finer granularity. They belong to the broader family of Likert-type items, one of the close-ended question formats, and are the standard tool for measuring attitudes, beliefs, and perceptions that don't reduce cleanly to a factual yes or no.

The format's core assumption: respondents can meaningfully locate their own attitude along an ordered scale, and that self-placement, aggregated across many respondents, reveals something real about how a population feels.

Why Researchers Use Them

• They measure attitude, not just behaviour: Where a yes/no question captures what happened, an agreement scale captures how someone feels about it, and feeling is frequently what predicts future behaviour
• They're fast to answer and fast to analyse: Respondents can process a statement and select a point on a scale in seconds, and the resulting data is immediately quantifiable
• They work well in batteries: Multiple related agreement statements can be grouped into a single, more reliable composite score, the multi-item scale logic covered in depth in ordinal scale
• They're familiar to respondents: The format is so widely used that almost no one needs it explained, reducing the cognitive load compared to a genuinely novel question type
• They translate cleanly into trend data: Locked wording and scale, tracked wave to wave, turn a single attitude measurement into a monitorable metric over time

When to Use Agree or Disagree Questions

• Measuring attitudes and beliefs: "I trust this brand" is naturally an agreement statement; it doesn't have an obvious factual yes/no form
• Building multi-item scales for a single construct: Several related statements about, say, job satisfaction, combined into one reliable composite score
• Tracking sentiment over time: Locked wording tracked wave to wave produces genuine trend data
• When the underlying question is inherently a matter of degree: "I would recommend this to a friend" captures intensity a binary yes/no cannot
• When NOT to use them: For factual questions with a clean, objectively correct answer (use closed factual questions instead), when you need to force a genuine choice between competing priorities (use ranking instead), or when the statement can't be written without embedding two ideas at once (the double-barrelled trap covered below)

50+ Agree or Disagree Question Examples

All items use a 5-point scale: Strongly disagree / Disagree / Neither agree nor disagree / Agree / Strongly agree, unless noted.

Customer Satisfaction

1. I am satisfied with my overall experience with this company.
2. The product met my expectations.
3. I received the support I needed when I had questions.
4. My issue was resolved in a reasonable amount of time.
5. I would consider this company reliable.

Product Feedback

1. This product is easy to use.
2. This product does what it claims to do.
3. I would miss this product if it were no longer available.
4. This product has improved since I first started using it.
5. I trust the quality of this product.

Employee Engagement

1. I understand what is expected of me in my role.
2. I have the resources I need to do my job well.
3. My manager supports my professional growth.
4. I feel recognised for the work I do.
5. I see a clear future for myself at this company.

Brand Perception

1. This brand understands people like me.
2. This brand is a leader in its category.
3. I feel proud to be associated with this brand.
4. This brand's values align with mine.
5. I would recommend this brand to someone I know.

Marketing Research

1. This advertisement caught my attention.
2. This message felt relevant to me personally.
3. I found this campaign easy to understand.
4. This offer felt like genuine value.
5. I trust the claims made in this advertisement.

Education

1. The course content matched what was promised at enrolment.
2. I felt confident applying what I learned.
3. The instructor explained concepts clearly.
4. The workload felt manageable alongside my other commitments.
5. I would recommend this course to a friend.

Healthcare

1. The doctor listened to my concerns fully.
2. I understood the instructions I was given for follow-up care.
3. The staff treated me with respect and courtesy.
4. I felt comfortable asking questions during my visit.
5. I trust the advice I received.

Workplace

1. Communication within my team is clear and consistent.
2. I feel comfortable sharing my opinions at work.
3. My workload is manageable most weeks.
4. I have opportunities to develop new skills here.
5. I believe leadership makes decisions in the company's best interest.

Events

1. The event content was relevant to my interests.
2. The event was well organised.
3. The speakers were engaging and knowledgeable.
4. The venue or platform worked well for this kind of event.
5. I would attend a similar event again.

General Opinion

1. I consider myself an early adopter of new technology.
2. Price is the most important factor in my purchase decisions.
3. I actively research products before buying them.
4. I trust online reviews when making decisions.
5. Sustainability influences my purchasing choices.

Using the bank: group 4-6 related statements into a single battery when measuring one underlying construct (satisfaction, trust, engagement), rather than scattering single unrelated items throughout a longer survey.

Common Mistakes

1. Leading statements: "Our award-winning support team resolves issues quickly" embeds the desired answer before the respondent has said anything: keep statements neutral
2. Double-barrelled statements: "This product is affordable and high quality" asks two questions in one: a respondent who agrees on price but disagrees on quality has no honest place to go
3. Biased or loaded wording: Adjectives that editorialise ("obviously," "clearly," "unfortunately") tilt the statement before the scale even applies
4. Missing the neutral midpoint: Forcing a choice between disagree and agree with no neutral option pushes genuinely undecided respondents toward a false answer
5. All-agree or all-disagree batteries: Writing every statement in the same direction invites acquiescence bias, the tendency to agree reflexively; mix positively and negatively worded items and reverse-score in analysis
6. Averaging without the caveats: Reporting a bare mean on a single agreement item skips the honest reporting discipline (distribution, median, top-2-box) covered in full in ordinal scale

Better Alternatives

Agree or disagree isn't always the right format. Consider these instead:

• Rating scales: When you need a direct quality or satisfaction judgment rather than agreement with a specific statement, a straightforward 1-5 rating is often more intuitive
• Ranking questions: When the goal is understanding priority among competing options, agreement scales let everything score high; rank order scales force the trade-off agreement scales can't
• Multiple choice: When there's a finite, known set of possible answers, a direct choice is faster and less ambiguous than agreement with a proxy statement
• Semantic differential scales: Bipolar adjective pairs (e.g., "Modern... Outdated") measuring perception along a spectrum, a format with real value for brand-perception research that this site doesn't yet cover in dedicated depth, a natural future addition to this cluster
• Open-ended questions: When you need the reasoning behind an attitude, not just its intensity, nothing replaces asking directly and reading the answer

Best Practices

• Keep every statement single-idea: One claim per statement, always: if a statement needs "and," split it into two
• Balance direction within a battery: Mix positively and negatively worded items to catch straight-lining, then reverse-score correctly in analysis
• Always include a neutral option: Unless you have a specific, justified reason to force a lean, "neither agree nor disagree" belongs on the scale
• Group related items into batteries: 4-6 statements measuring one construct produce a more reliable composite score than scattered single items
• Report honestly: Pair any mean with the distribution or top-2-box share, the reporting discipline that keeps agreement-scale findings trustworthy
• Pilot every new statement: Wording that seems neutral to the person writing it frequently reads as leading to someone outside the project; a small pilot catches this cheaply

Related Concepts

• Ordinal scale: The measurement-level depth behind Likert-type items: the legality of averaging, the "3.7 problem," and dual reporting
• Close-ended questions: The 7 structured formats agreement scales belong within
• Structured survey questions: The standardization discipline every agreement battery should follow
• Rank order scale: The alternative format when priority, not agreement, is the actual question
• Questionnaire question examples: The broader use-case-organised question bank this format-specific page complements
• Product survey questions: Where agreement-style questions appear inside a product-specific context
• Market research questions to ask potential customers: Pre-launch discovery questions, a different context from this page's post-launch and attitudinal focus

FAQs

1.What are agree or disagree questions?

Agree or disagree questions present a statement and ask respondents to rate their level of agreement, typically on a 5-point scale from "Strongly disagree" to "Strongly agree." They belong to the Likert-type question family and are the standard format for measuring attitudes, beliefs, and perceptions.

2.What is an example of an agree or disagree question?

"I am satisfied with my overall experience with this company," rated from Strongly disagree to Strongly agree, is a typical example. Other common examples include "This product is easy to use" and "I would recommend this brand to someone I know."

3.When should you use agree or disagree questions?

Use them to measure attitudes and beliefs that don't reduce cleanly to a factual yes or no, to build multi-item scales for a single construct like satisfaction or trust, and to track sentiment consistently over time. Avoid them for factual questions, forced trade-offs between priorities, or ideas that can't be stated as one clean claim.

4.What is the difference between agree/disagree questions and a Likert scale?

Agree or disagree questions are the most common type of Likert-type item; "Likert scale" more broadly refers to any ordered response scale used to measure attitude intensity, which can include frequency scales (never to always) or satisfaction scales (very dissatisfied to very satisfied) beyond just agreement wording specifically.

5.What mistakes should I avoid with agree or disagree questions?

Avoid leading statements that embed the desired answer, double-barrelled statements combining two ideas, biased or loaded wording, omitting the neutral midpoint, writing every item in the same direction (which invites reflexive agreement), and reporting a bare average without the distribution or median alongside it.

6.What are better alternatives to agree or disagree questions?

Rating scales work better for direct quality judgments, ranking questions force genuine trade-offs agreement scales can't, multiple choice suits questions with a known finite answer set, semantic differential scales suit brand-perception research, and open-ended questions are necessary when you need the reasoning behind an attitude, not just its strength.

7.Can you calculate an average on agree or disagree questions?

With caveats: a single item is technically ordinal data, so averaging assumes equal spacing between points that isn't guaranteed. In practice, means on well-designed 5-point balanced scales are widely used and track medians closely; the honest discipline is reporting the mean alongside the distribution or median, not instead of it.